{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "085feda9",
   "metadata": {},
   "source": [
    "## Resnet on Tiny ImageNet\n",
    "\n",
    "在[resnet.ipynb](./resnet.ipynb)中我们测试了Resnet在Imagenette上的分类任务, Imagenette是只包含10类的ImageNet的子集,这里我们在TinyImagenet上测试对比不同的Resnet网络效果.\n",
    "\n",
    "Tiny ImageNet 包含 100000 张图片，涵盖了200个类别（每个类别有500张图片），图片大小为64×64，并且为彩色图片。每个类别有500张训练图片，50张验证图片和50张测试图片。\n",
    "\n",
    "随机挑选的15个类别:\n",
    "\n",
    "![alt text](resources/tinyimagenet.png \"Title\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e15b3c33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 自动重新加载外部module，使得修改代码之后无需重新import\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from hdd.device.utils import get_device\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# 设置训练数据的路径\n",
    "DATA_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置TensorBoard的路径\n",
    "TENSORBOARD_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置预训练模型参数路径\n",
    "TORCH_HUB_PATH = \"~/workspace/hands-dirty-on-dl/pretrained_models\"\n",
    "torch.hub.set_dir(TORCH_HUB_PATH)\n",
    "# 挑选最合适的训练设备\n",
    "DEVICE = get_device([\"cuda\", \"cpu\"])\n",
    "print(\"Use device: \", DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c08db432",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Basic Info of train dataaset: \n",
      " Dataset TinyImagenet\n",
      "    Number of datapoints: 100000\n",
      "    Root location: /home/tf/workspace/hands-dirty-on-dl/dataset\n",
      "    StandardTransform\n",
      "Transform: Compose(\n",
      "               RandomCrop(size=(56, 56), padding=None)\n",
      "               ColorJitter(brightness=(0.9, 1.1), contrast=None, saturation=None, hue=None)\n",
      "               RandomHorizontalFlip(p=0.5)\n",
      "               ToTensor()\n",
      "               Normalize(mean=[0.4802, 0.4481, 0.3975], std=[0.2302, 0.2265, 0.2262])\n",
      "           )\n",
      "Basic Info of test dataset: \n",
      " Dataset TinyImagenet\n",
      "    Number of datapoints: 10000\n",
      "    Root location: /home/tf/workspace/hands-dirty-on-dl/dataset\n",
      "    StandardTransform\n",
      "Transform: Compose(\n",
      "               CenterCrop(size=(56, 56))\n",
      "               ToTensor()\n",
      "               Normalize(mean=[0.4802, 0.4481, 0.3975], std=[0.2302, 0.2265, 0.2262])\n",
      "           )\n"
     ]
    }
   ],
   "source": [
    "from hdd.dataset.tiny_imagenet import TinyImagenet\n",
    "import torchvision.transforms.v2\n",
    "\n",
    "# 提前计算好了均值和方差\n",
    "TRAIN_MEAN = [0.4802, 0.4481, 0.3975]\n",
    "TRAIN_STD = [0.2302, 0.2265, 0.2262]\n",
    "\n",
    "\n",
    "train_dataset_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.RandomCrop(56),\n",
    "        transforms.ColorJitter(0.1),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "    ]\n",
    ")\n",
    "train_dataset = TinyImagenet(\n",
    "    root=DATA_ROOT, split=\"train\", download=True, transform=train_dataset_transforms\n",
    ")\n",
    "val_dataset = TinyImagenet(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"val\",\n",
    "    download=True,\n",
    "    transform=transforms.Compose(\n",
    "        [\n",
    "            transforms.CenterCrop(56),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize(TRAIN_MEAN, TRAIN_STD),\n",
    "        ]\n",
    "    ),\n",
    ")\n",
    "print(\"Basic Info of train dataaset: \\n\", train_dataset)\n",
    "print(\"Basic Info of test dataset: \\n\", val_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6f5e2614",
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 64\n",
    "train_dataloader = torch.utils.data.DataLoader(\n",
    "    train_dataset,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    shuffle=True,\n",
    ")\n",
    "val_dataloader = torch.utils.data.DataLoader(\n",
    "    val_dataset,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    shuffle=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a9ad906",
   "metadata": {},
   "source": [
    "## 测试比较不同的Resnet架构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3e567083",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/120 Train Loss: 4.6324 Accuracy: 0.0568 Time: 45.97297  | Val Loss: 3.9824 Accuracy: 0.1311\n",
      "Epoch: 2/120 Train Loss: 3.9277 Accuracy: 0.1399 Time: 46.46360  | Val Loss: 3.5897 Accuracy: 0.2042\n",
      "Epoch: 3/120 Train Loss: 3.5212 Accuracy: 0.2047 Time: 45.35173  | Val Loss: 3.2178 Accuracy: 0.2570\n",
      "Epoch: 4/120 Train Loss: 3.2334 Accuracy: 0.2559 Time: 46.36122  | Val Loss: 2.9562 Accuracy: 0.3060\n",
      "Epoch: 5/120 Train Loss: 3.0049 Accuracy: 0.2987 Time: 46.55920  | Val Loss: 2.7445 Accuracy: 0.3492\n",
      "Epoch: 6/120 Train Loss: 2.8266 Accuracy: 0.3340 Time: 45.84230  | Val Loss: 2.6069 Accuracy: 0.3814\n",
      "Epoch: 7/120 Train Loss: 2.6716 Accuracy: 0.3661 Time: 45.88423  | Val Loss: 2.4709 Accuracy: 0.4105\n",
      "Epoch: 8/120 Train Loss: 2.5467 Accuracy: 0.3903 Time: 46.02452  | Val Loss: 2.3870 Accuracy: 0.4286\n",
      "Epoch: 9/120 Train Loss: 2.4274 Accuracy: 0.4147 Time: 46.04501  | Val Loss: 2.2947 Accuracy: 0.4465\n",
      "Epoch: 10/120 Train Loss: 2.3308 Accuracy: 0.4357 Time: 46.02318  | Val Loss: 2.2962 Accuracy: 0.4497\n",
      "Epoch: 11/120 Train Loss: 2.2451 Accuracy: 0.4536 Time: 45.80127  | Val Loss: 2.2102 Accuracy: 0.4715\n",
      "Epoch: 12/120 Train Loss: 2.1655 Accuracy: 0.4718 Time: 45.48431  | Val Loss: 2.1949 Accuracy: 0.4730\n",
      "Epoch: 13/120 Train Loss: 2.0980 Accuracy: 0.4841 Time: 45.25488  | Val Loss: 2.0771 Accuracy: 0.5010\n",
      "Epoch: 14/120 Train Loss: 2.0433 Accuracy: 0.4978 Time: 45.23982  | Val Loss: 2.1229 Accuracy: 0.4923\n",
      "Epoch: 15/120 Train Loss: 1.9832 Accuracy: 0.5084 Time: 45.24841  | Val Loss: 2.0891 Accuracy: 0.4943\n",
      "Epoch: 16/120 Train Loss: 1.9334 Accuracy: 0.5201 Time: 45.24208  | Val Loss: 2.0146 Accuracy: 0.5173\n",
      "Epoch: 17/120 Train Loss: 1.8909 Accuracy: 0.5303 Time: 45.27607  | Val Loss: 2.0345 Accuracy: 0.5141\n",
      "Epoch: 18/120 Train Loss: 1.8384 Accuracy: 0.5425 Time: 45.96840  | Val Loss: 2.0501 Accuracy: 0.5148\n",
      "Epoch: 19/120 Train Loss: 1.8061 Accuracy: 0.5493 Time: 45.99880  | Val Loss: 2.0542 Accuracy: 0.5062\n",
      "Epoch: 20/120 Train Loss: 1.7768 Accuracy: 0.5548 Time: 46.25011  | Val Loss: 2.0395 Accuracy: 0.5151\n",
      "Epoch: 21/120 Train Loss: 1.7396 Accuracy: 0.5618 Time: 46.46570  | Val Loss: 2.0583 Accuracy: 0.5133\n",
      "Epoch: 22/120 Train Loss: 1.7129 Accuracy: 0.5686 Time: 46.06617  | Val Loss: 2.0195 Accuracy: 0.5254\n",
      "Epoch: 23/120 Train Loss: 1.6884 Accuracy: 0.5751 Time: 45.74809  | Val Loss: 2.0375 Accuracy: 0.5202\n",
      "Epoch: 24/120 Train Loss: 1.6670 Accuracy: 0.5801 Time: 45.47712  | Val Loss: 1.9828 Accuracy: 0.5351\n",
      "Epoch: 25/120 Train Loss: 1.6397 Accuracy: 0.5851 Time: 45.22364  | Val Loss: 1.9971 Accuracy: 0.5306\n",
      "Epoch: 26/120 Train Loss: 1.6191 Accuracy: 0.5895 Time: 45.19850  | Val Loss: 1.9415 Accuracy: 0.5442\n",
      "Epoch: 27/120 Train Loss: 1.5921 Accuracy: 0.5954 Time: 45.23326  | Val Loss: 2.0196 Accuracy: 0.5244\n",
      "Epoch: 28/120 Train Loss: 1.5824 Accuracy: 0.5975 Time: 45.24971  | Val Loss: 2.1231 Accuracy: 0.5140\n",
      "Epoch: 29/120 Train Loss: 1.5635 Accuracy: 0.6013 Time: 45.22456  | Val Loss: 2.0050 Accuracy: 0.5243\n",
      "Epoch: 30/120 Train Loss: 1.5503 Accuracy: 0.6065 Time: 45.24113  | Val Loss: 2.0032 Accuracy: 0.5292\n",
      "Epoch: 31/120 Train Loss: 1.0995 Accuracy: 0.7118 Time: 45.23366  | Val Loss: 1.6935 Accuracy: 0.6026\n",
      "Epoch: 32/120 Train Loss: 0.9516 Accuracy: 0.7452 Time: 45.29046  | Val Loss: 1.7059 Accuracy: 0.6054\n",
      "Epoch: 33/120 Train Loss: 0.8820 Accuracy: 0.7622 Time: 45.21445  | Val Loss: 1.7141 Accuracy: 0.6106\n",
      "Epoch: 34/120 Train Loss: 0.8335 Accuracy: 0.7719 Time: 45.25334  | Val Loss: 1.7530 Accuracy: 0.6099\n",
      "Epoch: 35/120 Train Loss: 0.7883 Accuracy: 0.7838 Time: 45.26020  | Val Loss: 1.7784 Accuracy: 0.6084\n",
      "Epoch: 36/120 Train Loss: 0.7495 Accuracy: 0.7933 Time: 45.26032  | Val Loss: 1.7759 Accuracy: 0.6104\n",
      "Epoch: 37/120 Train Loss: 0.7164 Accuracy: 0.8028 Time: 45.25081  | Val Loss: 1.8035 Accuracy: 0.6098\n",
      "Epoch: 38/120 Train Loss: 0.6905 Accuracy: 0.8073 Time: 45.26640  | Val Loss: 1.8176 Accuracy: 0.6107\n",
      "Epoch: 39/120 Train Loss: 0.6652 Accuracy: 0.8145 Time: 45.23670  | Val Loss: 1.8437 Accuracy: 0.6070\n",
      "Epoch: 40/120 Train Loss: 0.6367 Accuracy: 0.8201 Time: 45.25016  | Val Loss: 1.8636 Accuracy: 0.6066\n",
      "Epoch: 41/120 Train Loss: 0.6103 Accuracy: 0.8280 Time: 45.21314  | Val Loss: 1.8882 Accuracy: 0.6060\n",
      "Epoch: 42/120 Train Loss: 0.5906 Accuracy: 0.8322 Time: 45.24493  | Val Loss: 1.8974 Accuracy: 0.6032\n",
      "Epoch: 43/120 Train Loss: 0.5664 Accuracy: 0.8380 Time: 45.21738  | Val Loss: 1.9352 Accuracy: 0.6050\n",
      "Epoch: 44/120 Train Loss: 0.5451 Accuracy: 0.8440 Time: 45.57282  | Val Loss: 1.9374 Accuracy: 0.6030\n",
      "Epoch: 45/120 Train Loss: 0.5264 Accuracy: 0.8469 Time: 45.62157  | Val Loss: 1.9829 Accuracy: 0.6057\n",
      "Epoch: 46/120 Train Loss: 0.5080 Accuracy: 0.8534 Time: 46.61306  | Val Loss: 2.0314 Accuracy: 0.6014\n",
      "Epoch: 47/120 Train Loss: 0.4912 Accuracy: 0.8560 Time: 46.09317  | Val Loss: 2.0244 Accuracy: 0.6020\n",
      "Epoch: 48/120 Train Loss: 0.4775 Accuracy: 0.8608 Time: 44.97602  | Val Loss: 2.0545 Accuracy: 0.5953\n",
      "Epoch: 49/120 Train Loss: 0.4613 Accuracy: 0.8650 Time: 44.90321  | Val Loss: 2.0853 Accuracy: 0.6022\n",
      "Epoch: 50/120 Train Loss: 0.4468 Accuracy: 0.8688 Time: 44.95413  | Val Loss: 2.1218 Accuracy: 0.5986\n",
      "Epoch: 51/120 Train Loss: 0.4316 Accuracy: 0.8734 Time: 44.93296  | Val Loss: 2.1265 Accuracy: 0.5964\n",
      "Epoch: 52/120 Train Loss: 0.4163 Accuracy: 0.8775 Time: 44.75601  | Val Loss: 2.1368 Accuracy: 0.5967\n",
      "Epoch: 53/120 Train Loss: 0.4051 Accuracy: 0.8801 Time: 45.06115  | Val Loss: 2.1649 Accuracy: 0.5949\n",
      "Epoch: 54/120 Train Loss: 0.3919 Accuracy: 0.8831 Time: 45.11196  | Val Loss: 2.1847 Accuracy: 0.5943\n",
      "Epoch: 55/120 Train Loss: 0.3811 Accuracy: 0.8863 Time: 45.21121  | Val Loss: 2.1854 Accuracy: 0.5915\n",
      "Epoch: 56/120 Train Loss: 0.3689 Accuracy: 0.8890 Time: 45.26417  | Val Loss: 2.2293 Accuracy: 0.5928\n",
      "Epoch: 57/120 Train Loss: 0.3586 Accuracy: 0.8937 Time: 45.26698  | Val Loss: 2.2176 Accuracy: 0.5948\n",
      "Epoch: 58/120 Train Loss: 0.3513 Accuracy: 0.8944 Time: 45.21577  | Val Loss: 2.2966 Accuracy: 0.5906\n",
      "Epoch: 59/120 Train Loss: 0.3358 Accuracy: 0.8989 Time: 45.23344  | Val Loss: 2.2567 Accuracy: 0.5897\n",
      "Epoch: 60/120 Train Loss: 0.3323 Accuracy: 0.8998 Time: 45.22945  | Val Loss: 2.3140 Accuracy: 0.5894\n",
      "Epoch: 61/120 Train Loss: 0.2829 Accuracy: 0.9156 Time: 45.23584  | Val Loss: 2.2919 Accuracy: 0.5935\n",
      "Epoch: 62/120 Train Loss: 0.2669 Accuracy: 0.9201 Time: 45.20128  | Val Loss: 2.2930 Accuracy: 0.5958\n",
      "Epoch: 63/120 Train Loss: 0.2588 Accuracy: 0.9225 Time: 45.21121  | Val Loss: 2.3013 Accuracy: 0.5947\n",
      "Epoch: 64/120 Train Loss: 0.2528 Accuracy: 0.9238 Time: 45.24445  | Val Loss: 2.2941 Accuracy: 0.5953\n",
      "Epoch: 65/120 Train Loss: 0.2505 Accuracy: 0.9254 Time: 45.25425  | Val Loss: 2.3491 Accuracy: 0.5925\n",
      "Epoch: 66/120 Train Loss: 0.2450 Accuracy: 0.9268 Time: 45.28265  | Val Loss: 2.3476 Accuracy: 0.5927\n",
      "Epoch: 67/120 Train Loss: 0.2405 Accuracy: 0.9278 Time: 45.24853  | Val Loss: 2.2877 Accuracy: 0.5931\n",
      "Epoch: 68/120 Train Loss: 0.2373 Accuracy: 0.9286 Time: 45.26519  | Val Loss: 2.3613 Accuracy: 0.5948\n",
      "Epoch: 69/120 Train Loss: 0.2352 Accuracy: 0.9295 Time: 45.23255  | Val Loss: 2.3746 Accuracy: 0.5931\n",
      "Epoch: 70/120 Train Loss: 0.2354 Accuracy: 0.9292 Time: 45.25340  | Val Loss: 2.3638 Accuracy: 0.5956\n",
      "Epoch: 71/120 Train Loss: 0.2317 Accuracy: 0.9300 Time: 45.49061  | Val Loss: 2.3977 Accuracy: 0.5912\n",
      "Early stop at epoch 71!\n",
      "#Parameter: 11271432 Accuracy: 0.6026\n"
     ]
    }
   ],
   "source": [
    "from spacy import training\n",
    "from hdd.models.cnn.resnet import ResnetSmall, resnet18_config\n",
    "from hdd.train.early_stopping import EarlyStoppingInMem\n",
    "from hdd.train.classification_utils import (\n",
    "    naive_train_classification_model,\n",
    "    eval_image_classifier,\n",
    ")\n",
    "from hdd.models.nn_utils import count_trainable_parameter\n",
    "\n",
    "\n",
    "def train_net(\n",
    "    resnet_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout,\n",
    "    lr,\n",
    "    weight_decay,\n",
    "    step_size=30,\n",
    "    gamma=0.1,\n",
    "    patience=40,\n",
    "    max_epochs=120,\n",
    ") -> tuple[ResnetSmall, dict[str, list[float]]]:\n",
    "    net = ResnetSmall(resnet_config, num_classes=200, dropout=dropout).to(DEVICE)\n",
    "    criteria = nn.CrossEntropyLoss()\n",
    "    # SGD的收敛速度远不如Adam好\n",
    "    # optimizer = torch.optim.SGD(\n",
    "    #     net.parameters(), lr=lr, momentum=0.9, weight_decay=weight_decay\n",
    "    # )\n",
    "    optimizer = optim.AdamW(\n",
    "        net.parameters(), lr=lr, eps=1e-6, weight_decay=weight_decay\n",
    "    )\n",
    "    scheduler = torch.optim.lr_scheduler.StepLR(\n",
    "        optimizer, step_size=step_size, gamma=gamma, last_epoch=-1\n",
    "    )\n",
    "    early_stopper = EarlyStoppingInMem(patience=patience, verbose=False)\n",
    "    training_stats = naive_train_classification_model(\n",
    "        net,\n",
    "        criteria,\n",
    "        max_epochs,\n",
    "        train_dataloader,\n",
    "        val_dataloader,\n",
    "        DEVICE,\n",
    "        optimizer,\n",
    "        scheduler,\n",
    "        early_stopper,\n",
    "        verbose=True,\n",
    "    )\n",
    "    return net, training_stats\n",
    "\n",
    "\n",
    "net, resnet18_stats = train_net(\n",
    "    resnet18_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout=0.5,\n",
    "    lr=0.005,\n",
    "    weight_decay=1e-2,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "65a27a4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/120 Train Loss: 4.6180 Accuracy: 0.0607 Time: 77.13795  | Val Loss: 3.9977 Accuracy: 0.1227\n",
      "Epoch: 2/120 Train Loss: 3.8806 Accuracy: 0.1470 Time: 76.83033  | Val Loss: 3.4955 Accuracy: 0.2134\n",
      "Epoch: 3/120 Train Loss: 3.5015 Accuracy: 0.2071 Time: 76.59613  | Val Loss: 3.2148 Accuracy: 0.2597\n",
      "Epoch: 4/120 Train Loss: 3.2591 Accuracy: 0.2523 Time: 77.45329  | Val Loss: 2.9946 Accuracy: 0.2992\n",
      "Epoch: 5/120 Train Loss: 3.0870 Accuracy: 0.2840 Time: 75.94696  | Val Loss: 2.9374 Accuracy: 0.3089\n",
      "Epoch: 6/120 Train Loss: 2.9577 Accuracy: 0.3093 Time: 76.10020  | Val Loss: 2.8393 Accuracy: 0.3317\n",
      "Epoch: 7/120 Train Loss: 2.8604 Accuracy: 0.3286 Time: 76.09025  | Val Loss: 2.8564 Accuracy: 0.3281\n",
      "Epoch: 8/120 Train Loss: 2.7813 Accuracy: 0.3437 Time: 76.08648  | Val Loss: 2.7285 Accuracy: 0.3567\n",
      "Epoch: 9/120 Train Loss: 2.7275 Accuracy: 0.3563 Time: 76.07937  | Val Loss: 2.6968 Accuracy: 0.3588\n",
      "Epoch: 10/120 Train Loss: 2.6802 Accuracy: 0.3662 Time: 76.08067  | Val Loss: 2.8512 Accuracy: 0.3375\n",
      "Epoch: 11/120 Train Loss: 2.6502 Accuracy: 0.3738 Time: 76.09583  | Val Loss: 2.6594 Accuracy: 0.3696\n",
      "Epoch: 12/120 Train Loss: 2.6132 Accuracy: 0.3802 Time: 76.28186  | Val Loss: 2.5722 Accuracy: 0.3883\n",
      "Epoch: 13/120 Train Loss: 2.5880 Accuracy: 0.3842 Time: 78.11267  | Val Loss: 2.5664 Accuracy: 0.3865\n",
      "Epoch: 14/120 Train Loss: 2.5673 Accuracy: 0.3905 Time: 76.53903  | Val Loss: 2.5609 Accuracy: 0.3913\n",
      "Epoch: 15/120 Train Loss: 2.5513 Accuracy: 0.3937 Time: 76.63880  | Val Loss: 2.6366 Accuracy: 0.3815\n",
      "Epoch: 16/120 Train Loss: 2.5317 Accuracy: 0.3987 Time: 76.87318  | Val Loss: 2.5720 Accuracy: 0.3929\n",
      "Epoch: 17/120 Train Loss: 2.5129 Accuracy: 0.4017 Time: 76.05944  | Val Loss: 2.6475 Accuracy: 0.3819\n",
      "Epoch: 18/120 Train Loss: 2.5004 Accuracy: 0.4048 Time: 76.09956  | Val Loss: 2.5526 Accuracy: 0.4001\n",
      "Epoch: 19/120 Train Loss: 2.4894 Accuracy: 0.4077 Time: 76.07744  | Val Loss: 2.9141 Accuracy: 0.3497\n",
      "Epoch: 20/120 Train Loss: 2.4742 Accuracy: 0.4106 Time: 76.06533  | Val Loss: 2.6400 Accuracy: 0.3806\n",
      "Epoch: 21/120 Train Loss: 2.4709 Accuracy: 0.4122 Time: 76.06731  | Val Loss: 2.6417 Accuracy: 0.3872\n",
      "Epoch: 22/120 Train Loss: 2.4648 Accuracy: 0.4122 Time: 76.05320  | Val Loss: 2.6421 Accuracy: 0.3889\n",
      "Epoch: 23/120 Train Loss: 2.4570 Accuracy: 0.4159 Time: 76.08964  | Val Loss: 2.5012 Accuracy: 0.4019\n",
      "Epoch: 24/120 Train Loss: 2.4437 Accuracy: 0.4192 Time: 76.06838  | Val Loss: 2.4303 Accuracy: 0.4199\n",
      "Epoch: 25/120 Train Loss: 2.4384 Accuracy: 0.4193 Time: 76.07473  | Val Loss: 2.5545 Accuracy: 0.3918\n",
      "Epoch: 26/120 Train Loss: 2.4318 Accuracy: 0.4211 Time: 76.08160  | Val Loss: 2.5663 Accuracy: 0.4011\n",
      "Epoch: 27/120 Train Loss: 2.4238 Accuracy: 0.4254 Time: 76.11718  | Val Loss: 2.5337 Accuracy: 0.4005\n",
      "Epoch: 28/120 Train Loss: 2.4193 Accuracy: 0.4232 Time: 76.12507  | Val Loss: 2.5261 Accuracy: 0.4030\n",
      "Epoch: 29/120 Train Loss: 2.4160 Accuracy: 0.4262 Time: 76.10246  | Val Loss: 2.4667 Accuracy: 0.4107\n",
      "Epoch: 30/120 Train Loss: 2.4122 Accuracy: 0.4250 Time: 76.11544  | Val Loss: 2.5591 Accuracy: 0.3975\n",
      "Epoch: 31/120 Train Loss: 1.7735 Accuracy: 0.5608 Time: 76.09465  | Val Loss: 1.7162 Accuracy: 0.5755\n",
      "Epoch: 32/120 Train Loss: 1.5599 Accuracy: 0.6081 Time: 76.09103  | Val Loss: 1.6635 Accuracy: 0.5889\n",
      "Epoch: 33/120 Train Loss: 1.4475 Accuracy: 0.6336 Time: 76.10050  | Val Loss: 1.6347 Accuracy: 0.6003\n",
      "Epoch: 34/120 Train Loss: 1.3504 Accuracy: 0.6553 Time: 76.08518  | Val Loss: 1.6382 Accuracy: 0.6006\n",
      "Epoch: 35/120 Train Loss: 1.2793 Accuracy: 0.6717 Time: 76.08695  | Val Loss: 1.6293 Accuracy: 0.6061\n",
      "Epoch: 36/120 Train Loss: 1.2069 Accuracy: 0.6863 Time: 76.06323  | Val Loss: 1.6222 Accuracy: 0.6029\n",
      "Epoch: 37/120 Train Loss: 1.1412 Accuracy: 0.7014 Time: 76.06757  | Val Loss: 1.6558 Accuracy: 0.6017\n",
      "Epoch: 38/120 Train Loss: 1.0873 Accuracy: 0.7139 Time: 76.08275  | Val Loss: 1.6728 Accuracy: 0.6015\n",
      "Epoch: 39/120 Train Loss: 1.0322 Accuracy: 0.7275 Time: 76.11330  | Val Loss: 1.6798 Accuracy: 0.6054\n",
      "Epoch: 40/120 Train Loss: 0.9798 Accuracy: 0.7388 Time: 76.09187  | Val Loss: 1.6981 Accuracy: 0.6101\n",
      "Epoch: 41/120 Train Loss: 0.9334 Accuracy: 0.7510 Time: 76.08805  | Val Loss: 1.7145 Accuracy: 0.6012\n",
      "Epoch: 42/120 Train Loss: 0.8905 Accuracy: 0.7612 Time: 76.10688  | Val Loss: 1.7550 Accuracy: 0.6008\n",
      "Epoch: 43/120 Train Loss: 0.8539 Accuracy: 0.7704 Time: 76.09349  | Val Loss: 1.7670 Accuracy: 0.5966\n",
      "Epoch: 44/120 Train Loss: 0.8187 Accuracy: 0.7768 Time: 76.07721  | Val Loss: 1.7781 Accuracy: 0.6031\n",
      "Epoch: 45/120 Train Loss: 0.7810 Accuracy: 0.7867 Time: 76.05188  | Val Loss: 1.8300 Accuracy: 0.5956\n",
      "Epoch: 46/120 Train Loss: 0.7534 Accuracy: 0.7935 Time: 76.15206  | Val Loss: 1.8519 Accuracy: 0.5909\n",
      "Epoch: 47/120 Train Loss: 0.7264 Accuracy: 0.8011 Time: 76.06392  | Val Loss: 1.8465 Accuracy: 0.5990\n",
      "Epoch: 48/120 Train Loss: 0.7087 Accuracy: 0.8050 Time: 76.09751  | Val Loss: 1.9059 Accuracy: 0.5926\n",
      "Epoch: 49/120 Train Loss: 0.6919 Accuracy: 0.8097 Time: 76.06935  | Val Loss: 1.9126 Accuracy: 0.5933\n",
      "Epoch: 50/120 Train Loss: 0.6722 Accuracy: 0.8125 Time: 76.04579  | Val Loss: 1.8764 Accuracy: 0.5983\n",
      "Epoch: 51/120 Train Loss: 0.6586 Accuracy: 0.8164 Time: 76.10252  | Val Loss: 1.9326 Accuracy: 0.5861\n",
      "Epoch: 52/120 Train Loss: 0.6490 Accuracy: 0.8182 Time: 76.06409  | Val Loss: 1.9697 Accuracy: 0.5858\n",
      "Epoch: 53/120 Train Loss: 0.6304 Accuracy: 0.8242 Time: 76.06240  | Val Loss: 1.9908 Accuracy: 0.5781\n",
      "Epoch: 54/120 Train Loss: 0.6237 Accuracy: 0.8241 Time: 76.05521  | Val Loss: 1.9947 Accuracy: 0.5820\n",
      "Epoch: 55/120 Train Loss: 0.6183 Accuracy: 0.8273 Time: 76.12013  | Val Loss: 2.0399 Accuracy: 0.5801\n",
      "Epoch: 56/120 Train Loss: 0.6104 Accuracy: 0.8292 Time: 76.06045  | Val Loss: 2.0077 Accuracy: 0.5847\n",
      "Epoch: 57/120 Train Loss: 0.6028 Accuracy: 0.8295 Time: 76.07480  | Val Loss: 2.0392 Accuracy: 0.5794\n",
      "Epoch: 58/120 Train Loss: 0.5956 Accuracy: 0.8344 Time: 76.09266  | Val Loss: 2.0207 Accuracy: 0.5764\n",
      "Epoch: 59/120 Train Loss: 0.5908 Accuracy: 0.8326 Time: 76.05967  | Val Loss: 2.0682 Accuracy: 0.5794\n",
      "Epoch: 60/120 Train Loss: 0.5922 Accuracy: 0.8348 Time: 76.10439  | Val Loss: 2.0855 Accuracy: 0.5765\n",
      "Epoch: 61/120 Train Loss: 0.3660 Accuracy: 0.8994 Time: 76.06734  | Val Loss: 1.8782 Accuracy: 0.6073\n",
      "Epoch: 62/120 Train Loss: 0.2902 Accuracy: 0.9212 Time: 76.07379  | Val Loss: 1.9148 Accuracy: 0.6068\n",
      "Epoch: 63/120 Train Loss: 0.2588 Accuracy: 0.9304 Time: 76.08966  | Val Loss: 1.9112 Accuracy: 0.6106\n",
      "Epoch: 64/120 Train Loss: 0.2366 Accuracy: 0.9374 Time: 76.07302  | Val Loss: 1.9403 Accuracy: 0.6141\n",
      "Epoch: 65/120 Train Loss: 0.2189 Accuracy: 0.9425 Time: 76.09440  | Val Loss: 1.9733 Accuracy: 0.6099\n",
      "Epoch: 66/120 Train Loss: 0.2053 Accuracy: 0.9455 Time: 76.07711  | Val Loss: 1.9768 Accuracy: 0.6085\n",
      "Epoch: 67/120 Train Loss: 0.1925 Accuracy: 0.9492 Time: 76.07251  | Val Loss: 2.0172 Accuracy: 0.6111\n",
      "Epoch: 68/120 Train Loss: 0.1825 Accuracy: 0.9514 Time: 76.09010  | Val Loss: 2.0262 Accuracy: 0.6107\n",
      "Epoch: 69/120 Train Loss: 0.1713 Accuracy: 0.9540 Time: 76.10398  | Val Loss: 2.0479 Accuracy: 0.6063\n",
      "Epoch: 70/120 Train Loss: 0.1593 Accuracy: 0.9579 Time: 76.08929  | Val Loss: 2.0866 Accuracy: 0.6087\n",
      "Epoch: 71/120 Train Loss: 0.1527 Accuracy: 0.9588 Time: 76.08011  | Val Loss: 2.0647 Accuracy: 0.6088\n",
      "Epoch: 72/120 Train Loss: 0.1464 Accuracy: 0.9610 Time: 76.08259  | Val Loss: 2.0907 Accuracy: 0.6046\n",
      "Epoch: 73/120 Train Loss: 0.1440 Accuracy: 0.9616 Time: 76.10064  | Val Loss: 2.1048 Accuracy: 0.6067\n",
      "Epoch: 74/120 Train Loss: 0.1346 Accuracy: 0.9636 Time: 76.07087  | Val Loss: 2.1409 Accuracy: 0.6063\n",
      "Epoch: 75/120 Train Loss: 0.1319 Accuracy: 0.9649 Time: 76.07978  | Val Loss: 2.1448 Accuracy: 0.6074\n",
      "Epoch: 76/120 Train Loss: 0.1243 Accuracy: 0.9672 Time: 76.09183  | Val Loss: 2.1761 Accuracy: 0.6063\n",
      "Early stop at epoch 76!\n",
      "#Parameter: 21379592 Accuracy: 0.603\n"
     ]
    }
   ],
   "source": [
    "from hdd.models.cnn.resnet import resnet34_config\n",
    "\n",
    "net, resnet34_stats = train_net(\n",
    "    resnet34_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout=0.5,\n",
    "    lr=0.005,\n",
    "    weight_decay=5e-2,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6310872a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,12))\n",
    "fields = resnet18_stats.keys()\n",
    "for i, field in enumerate(fields):\n",
    "    plt.subplot(2, 2, i+1)\n",
    "    plt.plot(resnet18_stats[field], label=\"Resnet18\", linestyle=\"--\")\n",
    "    plt.plot(resnet34_stats[field], label=\"Resnet34\")\n",
    "    plt.legend()\n",
    "    plt.title(field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b1276c75",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Plot train dataset.\n",
    "# %matplotlib inline\n",
    "# import matplotlib.pyplot as plt\n",
    "# plt.figure(figsize=(16,5))\n",
    "# images_per_class = 5\n",
    "# num_classes = 15\n",
    "\n",
    "# for target_class in range(num_classes):\n",
    "#     idxs = [idx  for idx, target  in enumerate(train_dataset.targets) if target == target_class]\n",
    "#     idxs = idxs[:images_per_class]\n",
    "#     for i, idx in enumerate(idxs):\n",
    "#         plt_idx = i * num_classes + target_class + 1\n",
    "#         plt.subplot(images_per_class, num_classes, plt_idx)\n",
    "#         image,_ = train_dataset[idx]\n",
    "#         plt.imshow(image)\n",
    "#         plt.axis(\"off\")\n",
    "#         if i == 0:\n",
    "#             plt.title(f\"{train_dataset.classes[target_class][0]}\", fontsize = \"x-small\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27d06953",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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